A Second-order Squeezed Wavelet Transform Algorithm for Seismic Signal Analysis

Li JIANG; Wenqing SHANG; Junni ZHOU; Mingfei WEI; Yanni WANG

doi:10.11999/JEIT200753

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3710-3717

Li JIANG, Wenqing SHANG, Junni ZHOU, Mingfei WEI, Yanni WANG. A Second-order Squeezed Wavelet Transform Algorithm for Seismic Signal Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3710-3717. doi: 10.11999/JEIT200753

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Li JIANG, Wenqing SHANG, Junni ZHOU, Mingfei WEI, Yanni WANG. A Second-order Squeezed Wavelet Transform Algorithm for Seismic Signal Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3710-3717. doi: 10.11999/JEIT200753

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A Second-order Squeezed Wavelet Transform Algorithm for Seismic Signal Analysis

doi: 10.11999/JEIT200753

College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

Funds: The National Natural Science Foundation of China (61803294, 61803293), Shaanxi Province Science and Technology Plan Project(2020JQ-684, 2020JM-499)

Received Date: 2020-08-25
Rev Recd Date: 2020-12-23

Available Online: 2021-02-27

Publish Date: 2021-12-21

Abstract

Abstract

Seismic signal is of great significance in the detection of geological lithology, reservoir, fluid and sedimentary facies, as well as the identification of stratigraphic interface, reservoir analysis, seismic data processing and interpretation. In view of the problems of low time-frequency resolution and poor energy aggregation when the traditional time-frequency analysis algorithms process seismic signals, a new 2nd-order Synchrosqueezing Wavelet Transform (SWT2) algorithm is proposed based on the model of Ricker wavelet. The proposed second-order squeezing algorithm uses the improved mother wavelet to match the seismic signals, and then corrects the reference frequency through spectral peak alignment, thus improving the time-frequency energy concentration and time-frequency resolution. Simulation results show that the proposed method can greatly improve the time-frequency aggregation, accurately reflect the time delay and dominant frequency of signals, and describe the stratigraphic structure more accurately.
- Signal processing,
- Seismic signal,
- Synchrosqueezing transform,
- Spectral decomposition

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References(16)

References

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